The authors generated synthetic tables using structural causal models, which encode relationships among variables as directed cause-and-effect mechanisms.
A synthetic-trained model predicts across tabular datasets without retraining
TabFM uses in-context learning to make zero-shot classification and regression predictions, reporting stronger aggregate TabArena performance than default foundation models and tuned AutoML pipelines.
Big Tech
Weihao Kong · Erez Louidor Ilan · Shuxin Nie · Taman Narayan · Rajat Sen · Yichen Zhou · +3 more
Google Research
Research Digest··3 min read
Kong et al.
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TabFM is a zero-shot foundation model for tabular data that outperforms tuned AutoML pipelines across 51 benchmarks.
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